6,526 research outputs found
Predicting Hospital Length of Stay in Intensive Care Unit
In this thesis, we investigate the performance of a series of classification methods for the
Prediction of the hospital Length of Stay (LoS) in Intensive Care Unit (ICU). Predicting
LOS for an inpatient in an hospital is a challenging task but is essential for the operational
success of a hospital. Since hospitals are faced with severely limited resources including
beds to hold admitted patients, prediction of LoS will assist the hospital staff for better
planning and management of hospital resources. The goal of this project is to create a
machine learning model that predicts the length-of stay for each patient at the time of
admission.
MIMIC-III database has been used for this project due to detailed information it contains
about ICU stays. MIMIC is an openly available dataset developed by the MIT Lab for
Computational Physiology, comprising de-identified health data associated with ~40,000
critical care patients at Beth Israel Deaconess Medical Centre. It includes demographics,
vital signs, laboratory tests, medications, and more.
Different machine learning techniques/classifiers have been investigated in this thesis. We
experimented with regression models as well as classification models with different classes
of varying granularity as target for LoS prediction. It turned out that granular classes (in
small unit of days) work better than regression models trying to predict exact duration in
days and hours. The overall performance of our classifiers was ranging from fair to very
good and has been discussed in the results. Secondly, we also experimented with building
separate LoS prediction models built for patients with different disease conditions and
compared it to the joint model built for all patients
Predicting Hospital Length of Stay in Intensive Care Unit
In this thesis, we investigate the performance of a series of classification methods for the
Prediction of the hospital Length of Stay (LoS) in Intensive Care Unit (ICU). Predicting
LOS for an inpatient in an hospital is a challenging task but is essential for the operational
success of a hospital. Since hospitals are faced with severely limited resources including
beds to hold admitted patients, prediction of LoS will assist the hospital staff for better
planning and management of hospital resources. The goal of this project is to create a
machine learning model that predicts the length-of stay for each patient at the time of
admission.
MIMIC-III database has been used for this project due to detailed information it contains
about ICU stays. MIMIC is an openly available dataset developed by the MIT Lab for
Computational Physiology, comprising de-identified health data associated with ~40,000
critical care patients at Beth Israel Deaconess Medical Centre. It includes demographics,
vital signs, laboratory tests, medications, and more.
Different machine learning techniques/classifiers have been investigated in this thesis. We
experimented with regression models as well as classification models with different classes
of varying granularity as target for LoS prediction. It turned out that granular classes (in
small unit of days) work better than regression models trying to predict exact duration in
days and hours. The overall performance of our classifiers was ranging from fair to very
good and has been discussed in the results. Secondly, we also experimented with building
separate LoS prediction models built for patients with different disease conditions and
compared it to the joint model built for all patients
PREDICTION OF SEPSIS DISEASE BY ARTIFICIAL NEURAL NETWORKS
Sepsis is a fatal condition, which affects at least 26 million people in the world every year that is resulted by an infection. For every 100,000 people, sepsis is seen in 149-240 of them and it has a mortality rate of 30%. The presence of infection in the patient is determined in order to diagnose the sepsis disease. Organ dysfunctions associated with an infection is diagnosed as sepsis. With the increased usage of artificial intelligence in the field of medicine, the early prediction and treatment of many diseases are provided with these methods. Considering the learning, reasoning and decision making abilities of artificial neural networks, which are the sub field of artificial intelligence are inferred to be used in predicting early stages of sepsis disease and determining the sepsis level is assessed. In this study, it is aimed to help sepsis diagnosis by using multi-layered artificial neural network.In construction of artificial neural network model, feed forward back propagation network structure and Levenberg-Marquardt training algorithm were used. The input and output variables of the model were the parameters which doctors use to diagnose the sepsis disease and determine the level of sepsis. The proposed method aims to provide an alternative prediction model for the early detection of sepsis disease
Performance Evaluation of Smart Decision Support Systems on Healthcare
Medical activity requires responsibility not only from clinical knowledge and skill but
also on the management of an enormous amount of information related to patient care. It is
through proper treatment of information that experts can consistently build a healthy wellness
policy. The primary objective for the development of decision support systems (DSSs) is
to provide information to specialists when and where they are needed. These systems provide
information, models, and data manipulation tools to help experts make better decisions in a
variety of situations.
Most of the challenges that smart DSSs face come from the great difficulty of dealing
with large volumes of information, which is continuously generated by the most diverse types
of devices and equipment, requiring high computational resources. This situation makes this
type of system susceptible to not recovering information quickly for the decision making. As a
result of this adversity, the information quality and the provision of an infrastructure capable
of promoting the integration and articulation among different health information systems (HIS)
become promising research topics in the field of electronic health (e-health) and that, for this
same reason, are addressed in this research. The work described in this thesis is motivated
by the need to propose novel approaches to deal with problems inherent to the acquisition,
cleaning, integration, and aggregation of data obtained from different sources in e-health environments,
as well as their analysis.
To ensure the success of data integration and analysis in e-health environments, it
is essential that machine-learning (ML) algorithms ensure system reliability. However, in this
type of environment, it is not possible to guarantee a reliable scenario. This scenario makes
intelligent SAD susceptible to predictive failures, which severely compromise overall system
performance. On the other hand, systems can have their performance compromised due to the
overload of information they can support.
To solve some of these problems, this thesis presents several proposals and studies
on the impact of ML algorithms in the monitoring and management of hypertensive disorders
related to pregnancy of risk. The primary goals of the proposals presented in this thesis are
to improve the overall performance of health information systems. In particular, ML-based
methods are exploited to improve the prediction accuracy and optimize the use of monitoring
device resources. It was demonstrated that the use of this type of strategy and methodology
contributes to a significant increase in the performance of smart DSSs, not only concerning precision
but also in the computational cost reduction used in the classification process.
The observed results seek to contribute to the advance of state of the art in methods
and strategies based on AI that aim to surpass some challenges that emerge from the integration
and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to
quickly and automatically analyze a larger volume of complex data and focus on more accurate
results, providing high-value predictions for a better decision making in real time and without
human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento
e na habilidade clÃnica, mas também na gestão de uma enorme quantidade de informações
relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações
que os especialistas podem consistentemente construir uma polÃtica saudável de bem-estar. O
principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações
aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações,
modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores
decisões em diversas situações.
A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade
de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos
tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação
torna este tipo de sistemas suscetÃvel a não recuperar a informação rapidamente para a
tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão
de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas
de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde
eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho
descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar
com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de
diferentes fontes em ambientes de e-saúde, bem como sua análise.
Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é
importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade
do sistema. No entanto, neste tipo de ambiente, não é possÃvel garantir um cenário
totalmente confiável. Esse cenário torna os SAD inteligentes suscetÃveis à presença de falhas
de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os
sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que
podem suportar.
Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e
estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos
relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta
tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os
métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o
uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo
de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD
inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional
utilizado no processo de classificação.
Os resultados observados buscam contribuir para o avanço do estado da arte em métodos
e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que
advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados
em inteligência artificial é possÃvel analisar de forma rápida e automática um volume maior de
dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana
Prognostic prediction models using Self-Attention for ICU patients developing acute kidney injury
Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2022The general growth and improved accessibility to electronic health records demands an identical level of
progress in terms of the research community regarding clinical models. The usage of machine learning
techniques is key to this development, and so they are increasingly being used in large medical databases
with the purpose of creating solutions that work for specified patients, no matter the task or the disease.
Acute kidney injury (AKI) is a broad disease defined by abrupt changes in renal function. AKI has
a high morbidity and mortality, with an increased focus on critically ill patients. The main goal of this
thesis is to study the development of AKI within a patient’s stay in the intensive care unit (ICU).
Data from the MIMIC-III database was used to collect information regarding the patients. After a
detailed exclusion criteria, those were evaluated in terms of AKI stages, with the purpose of predicting the
next value of AKI stage one hour after the sequence of information fed to the model. This can suggest the
capacity of the model at predicting the aggravation of a patient’s AKI condition. The sequences used have
hourly information for every feature, and were used sequences of 6h, 12h and 24h length. Self-attention
mechanisms were used to make the predictions, using an adaptation for multi-variate time series built
from the successfully used models on natural language processing (NLP) tasks.
The predictions on this work were made for two variations of the KDIGO classification system: one
where only the serum creatinine (SCr) criteria was taken into account to determine the patient’s AKI
stage, and other where both SCr and urine output (UO) were considered. While most works addressing
AKI only tend to use SCr values to determine the patient’s AKI condition, the results were compared
using both approaches and were better when using both SCr and UO. For those experiments, the model
achieved up to 68.05% accuracy predicting an episode of AKI, compared to the 66.67% accuracy achieved
using only SCr values, which outperformed state-of-the-art results for both cases.
Feature importance was also used for each dataset associated with the two variations of KDIGO
classification system to identify what were the most important features. Furthermore, final results were
compared when using all features versus only using the most 10 important ones
Disease diagnosis in smart healthcare: Innovation, technologies and applications
To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed
Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices
We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome. We used 166 records for training of computational simulations (training), 41 for documentation of computational simulations (validation), and 41 for reliability check of computational simulations (testing). The first five laboratory indices ranked by importance were Neutrophil-to-lymphocyte ratio, Lactate Dehydrogenase, Fibrinogen, Albumin, and D-Dimers. The best ANN based on these indices achieved accuracy 95.97%, precision 90.63%, sensitivity 93.55%. and F1-score 92.06%, verified in the validation cohort. Our preliminary findings reveal for the first time an ANN to predict ICU hospitalization accurately and early, using only 5 easily accessible laboratory indices
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